Abstract
Subsumption-Linear Q-Resolution (SLQR) is introduced for proving theorems from Quantified Boolean Formulas. It is an adaptation of SL-Resolution, which applies to propositional and first-order logic. In turn SL-Resolution is closely related to model elimination and tableau methods. A major difference from QDPLL (DPLL adapted for QBF) is that QDPLL guesses variable assignments, while SLQR guesses clauses.
In prenex QBF (PCNF, all quantifier operations are outermost) a propositional formula D is called a nontrivial consequence of a QBF \(\varPsi \) if \(\varPsi \) is true (has at least one model) and D is true in every model of \(\varPsi \). Due to quantifiers, one cannot simply negate D and look for a refutation, as in propositional and first-order logic. Most previous work has addressed only the case that D is the empty clause, which can never be a nontrivial consequence.
This paper shows that SLQR with the operations of resolution on both existential and universal variables as well as universal reduction is inferentially complete for closed PCNF that are free of asymmetric tautologies; i.e., if D is logically implied by \(\varPsi \), there is a SLQR derivation of D from \(\varPsi \). A weaker form called SLQR–ures omits resolution on universal variables. It is shown that SLQR–ures is not inferentially complete, but is refutationally complete for closed PCNF.
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Notes
- 1.
I.e., simplifications where one operand is 0 or 1.
- 2.
If the derived clause is not minimal, propositional resolution may derive a subsuming clause.
- 3.
If the derived clause is not minimal, propositional resolution may derive a subsuming clause.
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We thank the reviewers for their careful reading and suggestions for clarifying the paper.
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Van Gelder, A. (2023). Subsumption-Linear Q-Resolution for QBF Theorem Proving. In: Hansen, H.H., Scedrov, A., de Queiroz, R.J. (eds) Logic, Language, Information, and Computation. WoLLIC 2023. Lecture Notes in Computer Science, vol 13923. Springer, Cham. https://doi.org/10.1007/978-3-031-39784-4_23
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